DeepSolar: A Machine Learning Framework to Efficiently Construct a Solar Deployment Database in the United States
DeepSolar: A Machine Learning Framework to Efficiently Construct a Solar Deployment Database in the United States
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DOI：
10.1016/j.joule.2018.11.021
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发表时间：
2018-12-19
期刊：
影响因子：
39.8
通讯作者：
Rajagopal, Ram
中科院分区：
文献类型：
--
作者：
Yu, Jiafan;Wang, Zhecheng;Rajagopal, Ram
我们开发了DeepSolar，这是一个深度学习框架，可以分析卫星图像来识别太阳能光伏电池板的GPS位置和尺寸。利用其高准确性和可扩展性，我们为美国本土构建了一个全面的高保真太阳能部署数据库。我们通过发现住宅太阳能部署密度在人口密度为1,000人/平方英里时达到峰值，随着家庭年收入逐渐接近15万美元而增加，并且与代表收入不平等的基尼指数呈负相关，从而证明了其价值。我们发现了一个太阳辐射阈值（4.5千瓦时/米（2）/天），超过这个阈值，太阳能部署就被“触发”了。“此外，我们建立了一个精确的基于机器学习的预测模型，以估计人口普查区层面的太阳能部署密度。我们提供DeepSolar数据库作为研究人员，公用事业，太阳能开发商和政策制定者的公开资源，以进一步揭示太阳能部署模式，建立全面的经济和行为模型，并最终支持太阳能电力的采用和管理。
We developed DeepSolar, a deep learning framework analyzing satellite imagery to identify the GPS locations and sizes of solar photovoltaic panels. Leveraging its high accuracy and scalability, we constructed a comprehensive high-fidelity solar deployment database for the contiguous US. We demonstrated its value by discovering that residential solar deployment density peaks at a population density of 1,000 capita/mile 2, increases with annual household income asymptoting at similar to$150k, and has an inverse correlation with the Gini index representing income inequality. We uncovered a solar radiation threshold (4.5 kWh/m(2)/day) above which the solar deployment is "triggered.'' Furthermore, we built an accurate machine learning-based predictive model to estimate the solar deployment density at the census tract level. We offer the DeepSolar database as a publicly available resource for researchers, utilities, solar developers, and policymakers to further uncover solar deployment patterns, build comprehensive economic and behavioral models, and ultimately support the adoption and management of solar electricity.